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Catcher's Earned Run Average (CERA)
Meaningless Stat Or An Effective Measure?
by Chuck Rosciam, SABR Member


A discussion of the value and shortfalls of the statistic called Catcher's Earned Run Average (CERA).

INTRODUCTION

Most experts (meaning managers, coaches, pitchers and catchers) believe that the aspect of the catcher's job that has the most impact is his game-calling, that is, his ability to work with pitchers and help them throw more effectively. The standard and most acceptable measure for a pitcher is the Earned Run Average (ERA). Baseball is a game that has statistics for virtually everything, but there seems to be precious little time and energy devoted to measuring how well catchers perform at calling the game. Rather, we see catchersí defense measured by how many base stealers they throw out or how many passed balls or errors are charged against the backstop. A recent attempt at measuring a catcher's defensive skills is the CERA, which basically is the Earned Run Average of the battery (catcher and the pitchers on a team) for each specific catcher as compared to all other catchers and their batterymates.

†††††††††† The most comprehensive published study on the subject is Craig Wright's "Catcher's ERA" in his book The Diamond Appraised. Craig defined a process whereby catchers on the same team can be compared by how well a common set of pitchers perform with each catcher. That is, Catcher A's and Catcher B's CERA for Pitcher 1 are compared for the differences. The resultant CERA can be used to draw a conclusion as to the intrateam value among catchers.

 

PROBLEMS WITH CERA


However, there is a problem with this straight forward approach, as noted by Keith Woolner in his study published in Baseball Prospectus. The problem is sample size. When attempting to use "matched pitchers" for a team's catchers, there are wide fluctuations in the number of innings especially for the backup catchers. These variations between catchers' innings and hence their CERA may be "natural variation" attributed to simple chance or they might be the result of true game-calling ability.

Furthermore, there is the situation of the alternate (backup) catcher being used as a late inning substitute and paired with mop-up bullpen hurlers, generally in a losing cause. The starting catcher would have very few innings with these bullpen guys (usually with a high ERA) while the backup catcher would have few innings with the #1 and #2 starting pitchers (who usually have lower ERA's). Then there is the phenomena of Grag Maddux. When he pitched for Atlanta he preferred to throw to backup catcher Eddie Perez instead of the number one guy, Javy Lopez. Because of Maddux's preference and low ERA, this would preclude any matched pairings or if pairings were ignored the scales would tip in Perez's favor.

The next concern with CERA (and by no means the last) is the way that CERA is now being captured and presented in various publications which form the core of the CERA statistical library. The Bill James Handbook formerly published by STATS, Inc. and now ACTA, do not use matched pairings, but rather capture all of a catcher's innings and earned runs regardless of the pitchers involved. It is a raw total report that in and of itself is very misleading.

In Table 1. below you have two equal catchers (A and B) who have the very same CERA for each and every pitcher (1, 2, and 3) they caught. The only difference between the catchers is in the number of innings caught for each pitcher although their cumulative total innings are identical. "CATCHER A" only caught 50 innings with "PITCHER 2" (ERA of 4.50) while "CATCHER B" caught 110 innings and had the identical CERA. "CATCHER B" is penalized (in his cumulative CERA of 3.86) for doing the same job as "CATCHER A" only because of the way the CERA raw total statistic is formulated.

Table 1

 

  PITCHER 1 PITCHER 2 PITCHER3 Catcher Total
  Inn ER ERA Inn ER ERA Inn ER ERA C-Inn C-ER CERA
CATCHER A 100 40 3.60 50 25 4.50 60 18 2.70 210 83 3.56
CATCHER B 50 20 3.60 110 55 4.50 50 15 2.70 210 90 3.86
Pitcher Total 150 60 3.60 160 80 4.50 110 33 2.70 420 173 3.71



The summary columns (highlighted in green) would be what is published and used for analysis in comparing CATCHERS A & B. It would seem that CATCHER A with a 3.56 CERA is a far better defensive guy than CATCHER B. (CERA of 3.86) However, we know different by looking at the numbers inside the numbers. Both catchers are equal. Although Table 1. is completely fictional it does demonstrate one of the potential problems with using raw totals in computing CERA.

Do these problems make CERA an invalid measure for a catcher? The answer is YES and NO. Keith Woolner (Baseball Prospectus) concluded that there was no statistical significance between catchers' ERA and that the differences were purely a matter of chance variations or randomness. However, his study only used a subset of a subset of all catchers and pitchers because he only included batteries (pitchers and catchers) with 100 innings or more. He is correct in his conclusions, but only for the data set he chose to use. Perhaps a different approach using every catcher and pitcher in some weighted fashion would make CERA relevent. But, that's a lot of data to manipulate. Fortunately we have Retrosheet and its play-by-play data which eventually might give us true validity. In the meantime, we can use the raw CERA data (Innings & Earned Runs) to roughly describe comparisons and limit our convictions of the results by the known shortfalls.


PRESENTING THE "IFFY" CERA DATA


The following two tables list the TOP 25 CATCHERS in Best CERA for a Season and Cumulatively (data for the years 1990-2003 only). The list has been sorted by d-CERA which is the difference between an individual catcher's CERA and the other catcher's ERA (O-ERA). The O-ERA is computed as the Team ERA (T-ERA) components (INN and ER) minus the catcher's CERA components.

Table 2 - Season CERA Leaders (100 Games Caught Minimum)

NickName LastName Year Team GC INN C-ER CERA T-ER T-INN T-ERA O-ER O-INN O-ERA d ERA
MIKE PIAZZA 1994 LAN 104 860.7 376 3.932 477 1014.0 4.234 101 153.3 5.928 -1.996
PAUL LODUCA 2003 LAN 123 1080.0 327 2.725 511 1457.7 3.155 184 377.7 4.385 -1.660
JASON KENDALL 2003 PIT 146 1278.3 635 4.471 744 1444.3 4.636 109 166.0 5.910 -1.439
BENITO SANTIAGO 2001 SFN 130 1080.0 461 3.842 680 1463.3 4.182 219 383.3 5.142 -1.300
A.J. PIERZYNSKI 2003 MIN 135 1165.7 537 4.146 716 1461.7 4.409 179 296.0 5.443 -1.296
BRAD AUSMUS 2001 HOU 127 1056.7 472 4.020 707 1454.7 4.374 235 398.0 5.314 -1.294
CHARLES JOHNSON 1996 FLO 120 998.0 395 3.562 633 1443.0 3.948 238 445.0 4.813 -1.251
JOE GIRARDI 1997 NYA 111 979.3 374 3.437 626 1467.0 3.840 252 487.7 4.651 -1.214
CHRIS HOILES 1995 BAL 107 871.7 381 3.934 607 1267.0 4.312 226 395.3 5.145 -1.211
JOE GIRARDI 1995 COL 122 1044.3 550 4.740 711 1288.0 4.968 161 243.7 5.947 -1.207
MIKE PIAZZA 1996 LAN 146 1255.7 462 3.311 567 1466.0 3.481 105 210.3 4.493 -1.181
BRENT MAYNE 2003 KCA 112 948.0 494 4.690 809 1438.7 5.061 315 490.7 5.778 -1.088
JORGE POSADA 2001 NYA 131 1111.7 466 3.773 649 1451.3 4.025 183 339.7 4.849 -1.076
JASON VARITEK 2000 BOS 128 1076.0 473 3.956 683 1452.3 4.232 210 376.3 5.022 -1.066
DAVE NILSSON 1999 ML4 101 762.0 387 4.571 812 1442.3 5.067 425 680.3 5.622 -1.051
DARRIN FLETCHER 1998 TOR 121 971.3 425 3.938 698 1465.0 4.288 273 493.7 4.977 -1.039
DAMIAN MILLER 2001 ARI 121 978.0 384 3.534 627 1459.7 3.866 243 481.7 4.540 -1.007
JOE OLIVER 1997 CIN 106 837.0 372 4.000 712 1449.0 4.422 340 612.0 5.000 -1.000
MIKE PIAZZA 2000 NYN 124 1026.3 441 3.867 670 1450.0 4.159 229 423.7 4.865 -0.997
BRAD AUSMUS 2003 HOU 143 1158.0 471 3.661 622 1450.0 3.861 151 292.0 4.654 -0.993
BRIAN HARPER 1993 MIN 134 1124.7 562 4.497 756 1444.0 4.712 194 319.3 5.468 -0.970
A.J. HINCH 1998 OAK 118 940.3 471 4.508 770 1434.0 4.833 299 493.7 5.451 -0.943
HENRY BLANCO 2001 ML4 102 837.3 395 4.246 740 1436.3 4.637 345 599.0 5.184 -0.938
TODD HUNDLEY 1992 NYN 121 892.3 328 3.308 588 1446.0 3.660 260 553.7 4.226 -0.918
BENITO SANTIAGO 1996 PHI 114 982.0 459 4.207 710 1423.0 4.491 251 441.0 5.122 -0.916


Table 3 - Cumulative 1990-2003 CERA Leaders (500 Games Caught Minimum)

NickName LastName GC C-INN C-ER CERA T-ER T-INN T-ERA O-ER O-INN O-ERA d ERA
DAMIAN MILLER 584 4715.3 2020 3.856 4727 10139.0 4.196 2707 5423.7 4.492 -0.636
RAMON HERNANDEZ 591 4808.0 2047 3.832 3237 7230.0 4.029 1190 2422.0 4.422 -0.590
MIKE PIAZZA 1379 11636.6 4911 3.798 8680 19706.3 3.964 3769 8069.7 4.204 -0.405
JASON VARITEK 657 5311.7 2353 3.987 3989 8683.3 4.134 1636 3371.7 4.367 -0.380
BENITO SANTIAGO 1338 11047.0 4862 3.961 8314 18211.3 4.109 3452 7164.4 4.336 -0.375
BRAD AUSMUS 1296 10743.3 5000 4.189 8081 16830.0 4.321 3081 6086.7 4.556 -0.367
IVAN RODRIGUEZ 1564 13075.7 6858 4.720 9725 18182.3 4.814 2867 5106.7 5.053 -0.332
TONY PENA 633 4858.7 2096 3.883 4918 10991.0 4.027 2822 6132.3 4.142 -0.259
LENNY WEBSTER 512 3496.3 1564 4.026 7163 15301.0 4.213 5599 11804.7 4.269 -0.243
JOE OLIVER 868 6912.7 3225 4.199 8720 18182.0 4.316 5495 11269.3 4.388 -0.190
KIRT MANWARING 858 6825.7 3386 4.465 7014 13858.7 4.555 3628 7033.0 4.643 -0.178
SCOTT SERVAIS 792 6210.3 2869 4.158 8582 18149.7 4.256 5713 11939.3 4.307 -0.149
MIKE MATHENY 985 7561.7 3731 4.441 6922 13864.3 4.493 3191 6302.7 4.557 -0.116
RICK WILKINS 649 5039.0 2358 4.212 9333 19556.0 4.295 6975 14517.0 4.324 -0.113
RON KARKOVICE 666 5100.3 2354 4.154 4460 9571.3 4.194 2106 4471.0 4.239 -0.085
TOM PAGNOZZI 673 5676.0 2516 3.989 4938 11028.3 4.030 2422 5352.3 4.073 -0.083
CHRIS HOILES 809 6706.7 3249 4.360 5362 10987.0 4.392 2113 4280.3 4.443 -0.083
TERRY STEINBACH 999 8276.0 4359 4.740 6558 12379.3 4.768 2199 4103.3 4.823 -0.083
JORGE POSADA 820 6830.6 3115 4.104 5328 11592.0 4.137 2213 4761.4 4.183 -0.079
CHARLES JOHNSON 1050 8839.0 4406 4.486 8401 16718.7 4.522 3995 7879.7 4.563 -0.077
BILL HASELMAN 521 3768.7 2055 4.908 8405 15242.0 4.963 6350 11473.3 4.981 -0.074
TONY EUSEBIO 522 4031.3 1832 4.090 5759 12529.3 4.137 3927 8498.0 4.159 -0.069
BRENT MAYNE 1061 8326.6 4255 4.599 10133 19674.7 4.635 5878 11348.0 4.662 -0.063
MIKE MACFARLANE 811 6455.3 3162 4.408 6846 13882.0 4.438 3684 7426.7 4.464 -0.056
KELLY STINNETT 536 4208.3 2042 4.367 7499 15317.0 4.406 5457 11108.7 4.421 -0.054

FINALE

The future of a game-calling measure, whether it be CERA or RPR (Run Prevention Rate) or some other formula derived from the play-by-play numbers, will still have to answer two questions:

  • Do the differences in game-calling measures (ie. CERA) among catchers vary from what we'd expect solely from chance or are the variations statistically significant?
  • Are the year-to-year game-calling measures for a catcher capable of being trended?

A catcher gains game-calling ability with time. The longer he's in the majors the more he's learned about his pitchers and the opposing hitters. A catcher must remember every pitch sequence to every hitter so as to avoid being predictable, a catalog that might stretch back several innings or several years. The longer he catches the more game-calling ability he possesses, which should show up in the numbers - some numbers somewhere. And that is the challenge - To identify the right numbers and assemble them in the right way.





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